Second World War fighter pilot Eric Carter is one of only four surviving members of a secret mission, code-named 'Force Benedict'. Sanctioned by Winston Churchill in 1941 Force Benedict was dispatched to defend Murmansk, the USSR's only port not under Nazi occupation. If Murmansk fell, Soviet resistance against the Nazis would be hard to sustain and Hitler would be able to turn all his forces on Britain...Force Benedict was under the command of New Zealand-born RAF Wing Commander Henry Neville Gynes Ramsbottom-Isherwood, who led two squadrons of Hurricane fighters, pilots and ground crew which were shipped to Russia in total secrecy on the first ever Arctic Convoy. They were told to defend Murmansk against the Germans 'at all costs'. 'We all reckoned the government thought we'd never survive' - but Eric Carter did, and was threatened with Court Martial if he talked about where he'd been or what he'd done. Now he reveals his experiences of seventy years ago in the hell on earth that was Murmansk, the largest city north of the Arctic Circle. It will also include previously unseen photos and documents, as well as exploring - for the first time - other intriguing aspects of Force Benedict.

Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.